Abstract: Deep-sea mining is a critical component of China’s resource strategy. The detection of polymetallic nodule sites in the Pacific Ocean provides essential guidance for future mineral extraction. To achieve efficient and accurate detection of deep-sea substrate layers, this paper proposes a multi-mechanism fused simulated annealing inversion method for acoustic parameters of deep-sea substrates utilizing external acoustic sources. Firstly, to mitigate the impact of a fixed neighborhood in the traditional simulated annealing algorithm on solution quality, an adaptive neighborhood adjustment mechanism is introduced, dynamically modifying the neighborhood radius based on temperature and the current solution’s quality. Secondly, addressing the issues of the traditional exponential cooling strategy—where rapid early cooling limits global exploration and slow later cooling causes solver oscillations—a multi-stage annealing strategy is implemented. This strategy divides the cooling process into high-, medium-, and low-temperature phases: exponential cooling in the high-temperature phase facilitates broad exploration by accepting inferior solutions with high probability; adaptive cooling in the medium-temperature phase slows the cooling rate to maintain global search capability and avoid local optima; linear cooling in the low-temperature phase accelerates convergence, conserving computational resources. Finally, to overcome the difficulty of escaping local optima with the traditional Metropolis criterion during the low-temperature phase, a dynamic acceptance criterion is incorporated to increase the probability of accepting inferior solutions, thereby enhancing the algorithm’s ability to jump out of local optima in the final stages and improving the inversion success rate. Simulations based on hydroacoustic detection data from the Clarion-Clipperton Zone （CCZ） in the East Pacific demonstrate the algorithm’s superior convergence rate. In 30 tests under identical conditions, the average number of inversion iterations is reduced by 32%, effectively shortening computation time and supporting rapid remote detection of deep-sea substrate acoustic parameters.